'Freidman test not working and may not be appropriate?
I want to know if there is a difference between mean distance between pairs of individuals across months. This is a repeated measures situation with a non normally distributed response variable. So, I am trying to run a Friedman test, but I can't get it to work and do not understand the error. I have made sure each pair within month is unreplicated. Any advice? Thanks
dput(head(cohorts,10))
structure(list(DeerCow = structure(c(1L, 1L, 1L, 2L, 2L, 3L,
3L, 3L, 4L, 4L), .Label = c("A82118_A632", "A82118_A633", "A82118_A635",
"A82118_A636", "A82120_A629", "A82120_A630", "A82120_A631", "A82120_A633",
"A82120_A634", "A82120_A637", "A82121_A628", "A82121_A629", "A82121_A630",
"A82121_A631", "A82121_A633", "A82121_A634", "A82121_A637", "A82122_A632",
"A82122_A633", "A82122_A635", "A82122_A636", "A82126_A629", "A82126_A630",
"A82126_A631", "A82126_A633", "A82126_A634", "A82126_A637", "A82127_A628",
"A82127_A632", "A82127_A633", "A82127_A636", "A82132_A628", "A82132_A629",
"A82132_A630", "A82132_A631", "A82132_A632", "A82132_A633", "A82132_A636",
"A82135_A629", "A82135_A630", "A82135_A631", "A82135_A632", "A82135_A633",
"A82135_A634", "A82135_A635", "A82135_A636", "A82135_A637", "A82136_A628",
"A82136_A630", "A82136_A631", "A82136_A632", "A82136_A634", "A82136_A636",
"A82139_A628", "A82139_A629", "A82139_A630", "A82139_A631", "A82139_A633",
"A82139_A634", "A82139_A637", "A82140_A628", "A82140_A629", "A82140_A630",
"A82140_A631", "A82140_A632", "A82140_A633", "A82140_A634", "A82140_A636",
"A82140_A637", "A82141_A632", "A82141_A633", "A82141_A635", "A82141_A636",
"A82142_A628", "A82142_A630", "A82142_A631", "A82142_A632", "A82142_A633",
"A82142_A634", "A82142_A636", "A82145_A628", "A82145_A630", "A82145_A631",
"A82145_A632", "A82145_A633", "A82145_A634", "A82145_A636", "A82146_A628",
"A82146_A629", "A82146_A631", "A82146_A632", "A82146_A633", "A82146_A634",
"A82146_A635", "A82146_A636", "A82146_A637"), class = "factor"),
YearMonth = structure(c(1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L,
2L), .Label = c("2019-06", "2019-07", "2019-08", "2019-09"
), class = "factor"), Mean_dist_m = c(1612.14219548009, 1870.02550251614,
2108.91771044161, 2435.58097649919, 2909.78460371203, 3311.10358068401,
2661.09192488057, 2378.49371438137, 1336.20465251428, 1637.15035958527
)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -10L), groups = structure(list(DeerCow = structure(1:4, .Label = c("A82118_A632",
"A82118_A633", "A82118_A635", "A82118_A636", "A82120_A629", "A82120_A630",
"A82120_A631", "A82120_A633", "A82120_A634", "A82120_A637", "A82121_A628",
"A82121_A629", "A82121_A630", "A82121_A631", "A82121_A633", "A82121_A634",
"A82121_A637", "A82122_A632", "A82122_A633", "A82122_A635", "A82122_A636",
"A82126_A629", "A82126_A630", "A82126_A631", "A82126_A633", "A82126_A634",
"A82126_A637", "A82127_A628", "A82127_A632", "A82127_A633", "A82127_A636",
"A82132_A628", "A82132_A629", "A82132_A630", "A82132_A631", "A82132_A632",
"A82132_A633", "A82132_A636", "A82135_A629", "A82135_A630", "A82135_A631",
"A82135_A632", "A82135_A633", "A82135_A634", "A82135_A635", "A82135_A636",
"A82135_A637", "A82136_A628", "A82136_A630", "A82136_A631", "A82136_A632",
"A82136_A634", "A82136_A636", "A82139_A628", "A82139_A629", "A82139_A630",
"A82139_A631", "A82139_A633", "A82139_A634", "A82139_A637", "A82140_A628",
"A82140_A629", "A82140_A630", "A82140_A631", "A82140_A632", "A82140_A633",
"A82140_A634", "A82140_A636", "A82140_A637", "A82141_A632", "A82141_A633",
"A82141_A635", "A82141_A636", "A82142_A628", "A82142_A630", "A82142_A631",
"A82142_A632", "A82142_A633", "A82142_A634", "A82142_A636", "A82145_A628",
"A82145_A630", "A82145_A631", "A82145_A632", "A82145_A633", "A82145_A634",
"A82145_A636", "A82146_A628", "A82146_A629", "A82146_A631", "A82146_A632",
"A82146_A633", "A82146_A634", "A82146_A635", "A82146_A636", "A82146_A637"
), class = "factor"), .rows = structure(list(1:3, 4:5, 6:8, 9:10), ptype = integer(0), class = c("vctrs_list_of",
"vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -4L), .drop = TRUE))
hist(cohorts$Mean_dist_m)
friedman.test(y=cohorts$Mean_dist_m, groups=cohorts$YearMonth, blocks=cohorts$DeerCow)
Error in friedman.test.default(y = cohorts$Mean_dist_m, groups = cohorts$YearMonth, :
not an unreplicated complete block design
Solution 1:[1]
If you look at the code, the part that is throwing the error is:
if (any(table(groups, blocks) != 1))
stop("not an unreplicated complete block design")
If you look at your data and do the appropriate table() operation, you get:
table(cohorts$YearMonth, cohorts$DeerCow)
# A82118_A632 A82118_A633 A82118_A635 A82118_A636 A82120_A629 A82120_A630 A82120_A631 A82120_A633
# 2019-06 1 1 1 1 0 0 0 0
# 2019-07 1 1 1 1 0 0 0 0
# 2019-08 1 0 1 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82120_A634 A82120_A637 A82121_A628 A82121_A629 A82121_A630 A82121_A631 A82121_A633 A82121_A634
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82121_A637 A82122_A632 A82122_A633 A82122_A635 A82122_A636 A82126_A629 A82126_A630 A82126_A631
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82126_A633 A82126_A634 A82126_A637 A82127_A628 A82127_A632 A82127_A633 A82127_A636 A82132_A628
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82132_A629 A82132_A630 A82132_A631 A82132_A632 A82132_A633 A82132_A636 A82135_A629 A82135_A630
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82135_A631 A82135_A632 A82135_A633 A82135_A634 A82135_A635 A82135_A636 A82135_A637 A82136_A628
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82136_A630 A82136_A631 A82136_A632 A82136_A634 A82136_A636 A82139_A628 A82139_A629 A82139_A630
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82139_A631 A82139_A633 A82139_A634 A82139_A637 A82140_A628 A82140_A629 A82140_A630 A82140_A631
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82140_A632 A82140_A633 A82140_A634 A82140_A636 A82140_A637 A82141_A632 A82141_A633 A82141_A635
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82141_A636 A82142_A628 A82142_A630 A82142_A631 A82142_A632 A82142_A633 A82142_A634 A82142_A636
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82145_A628 A82145_A630 A82145_A631 A82145_A632 A82145_A633 A82145_A634 A82145_A636 A82146_A628
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
#
# A82146_A629 A82146_A631 A82146_A632 A82146_A633 A82146_A634 A82146_A635 A82146_A636 A82146_A637
# 2019-06 0 0 0 0 0 0 0 0
# 2019-07 0 0 0 0 0 0 0 0
# 2019-08 0 0 0 0 0 0 0 0
# 2019-09 0 0 0 0 0 0 0 0
The output is so long because you still have all the factor levels attached. If you use droplevels(), you'll see that you still don't pass the test:
table(droplevels(cohorts$YearMonth), droplevels(cohorts$DeerCow))
# A82118_A632 A82118_A633 A82118_A635 A82118_A636
# 2019-06 1 1 1 1
# 2019-07 1 1 1 1
# 2019-08 1 0 1 0
If you filter the data to exclude 2019-08, then you pass the test and the Friedman.test() function works.
tmp <- subset(cohorts, YearMonth != "2019-08")
table(droplevels(tmp$YearMonth), droplevels(tmp$DeerCow))
# A82118_A632 A82118_A633 A82118_A635 A82118_A636
# 2019-06 1 1 1 1
# 2019-07 1 1 1 1
friedman.test(y=tmp$Mean_dist_m, groups=droplevels(tmp$YearMonth), blocks=droplevels(tmp$DeerCow))
# Friedman rank sum test
#
# data: tmp$Mean_dist_m, droplevels(tmp$YearMonth) and droplevels(tmp$DeerCow)
# Friedman chi-squared = 1, df = 1, p-value = 0.3173
The key is to look at the cross-tabulation of group and block variables and if there are any values in the table that are note 1, then your design does not meet the requirements of Friedman.test().
Sources
This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.
Source: Stack Overflow
| Solution | Source |
|---|---|
| Solution 1 | DaveArmstrong |